Automated stopping criterion for spectral measurements with active learning
Abstract: The automated stopping of a spectral measurement with active learning is proposed. The optimal stopping of the measurement is realised with a stopping criterion based on the upper bound of the posterior average of the generalisation error of the Gaussian process regression. It is revealed that the automated stopping criterion of the spectral measurement gives an approximated X-ray absorption spectrum with sufficient accuracy and reduced data size. The proposed method is not only a proof-of-concept of the optimal stopping problem in active learning but also the key to enhancing the efficiency of spectral measurements for high-throughput experiments in the era of materials informatics.
Keywords: Characterization and analytical techniques / Materials Science / general / Characterization and Evaluation of Materials / Mathematical and Computational Engineering / Theoretical / Mathematical and Computational Physics / Computational Intelligence / Mathematical Modeling and Industrial Mathematics
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